Python 如何在 matplotlib 中制作按密度着色的散点图？
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How can I make a scatter plot colored by density in matplotlib?
提问by 2964502
I'd like to make a scatter plot where each point is colored by the spatial density of nearby points.
我想制作一个散点图，其中每个点都由附近点的空间密度着色。
I've come across a very similar question, which shows an example of this using R:
我遇到了一个非常相似的问题，它显示了一个使用 R 的例子：
R Scatter Plot: symbol color represents number of overlapping points
What's the best way to accomplish something similar in python using matplotlib?
使用 matplotlib 在 python 中完成类似操作的最佳方法是什么？
采纳答案by Joe Kington
In addition to hist2dor hexbinas @askewchan suggested, you can use the same method that the accepted answer in the question you linked to uses.
除了@askewchan 建议之外hist2d或hexbin如@askewchan 建议的那样，您可以使用与链接到的问题中已接受的答案相同的方法。
If you want to do that:
如果你想这样做：
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
# Generate fake data
x = np.random.normal(size=1000)
y = x * 3 + np.random.normal(size=1000)
# Calculate the point density
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)
fig, ax = plt.subplots()
ax.scatter(x, y, c=z, s=100, edgecolor='')
plt.show()
If you'd like the points to be plotted in order of density so that the densest points are always on top (similar to the linked example), just sort them by the z-values. I'm also going to use a smaller marker size here as it looks a bit better:
如果您希望按密度顺序绘制点，以便最密集的点始终位于顶部（类似于链接示例），只需按 z 值对它们进行排序。我还将在这里使用较小的标记尺寸，因为它看起来更好一些：
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
# Generate fake data
x = np.random.normal(size=1000)
y = x * 3 + np.random.normal(size=1000)
# Calculate the point density
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)
# Sort the points by density, so that the densest points are plotted last
idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]
fig, ax = plt.subplots()
ax.scatter(x, y, c=z, s=50, edgecolor='')
plt.show()
回答by askewchan
You could make a histogram:
你可以做一个直方图：
import numpy as np
import matplotlib.pyplot as plt
# fake data:
a = np.random.normal(size=1000)
b = a*3 + np.random.normal(size=1000)
plt.hist2d(a, b, (50, 50), cmap=plt.cm.jet)
plt.colorbar()
回答by Guillaume
Also, if the number of point makes KDE calculation too slow, color can be interpolated in np.histogram2d [Update in response to comments: If you wish to show the colorbar, use plt.scatter() instead of ax.scatter() followed by plt.colorbar()]:
此外，如果点数使 KDE 计算速度过慢，则可以在 np.histogram2d 中插入颜色 [根据评论更新：如果您希望显示颜色条，请使用 plt.scatter() 而不是 ax.scatter()通过 plt.colorbar()]：
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import Normalize
from scipy.interpolate import interpn
def density_scatter( x , y, ax = None, sort = True, bins = 20, **kwargs ) :
"""
Scatter plot colored by 2d histogram
"""
if ax is None :
fig , ax = plt.subplots()
data , x_e, y_e = np.histogram2d( x, y, bins = bins, density = True )
z = interpn( ( 0.5*(x_e[1:] + x_e[:-1]) , 0.5*(y_e[1:]+y_e[:-1]) ) , data , np.vstack([x,y]).T , method = "splinef2d", bounds_error = False)
#To be sure to plot all data
z[np.where(np.isnan(z))] = 0.0
# Sort the points by density, so that the densest points are plotted last
if sort :
idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]
ax.scatter( x, y, c=z, **kwargs )
norm = Normalize(vmin = np.min(z), vmax = np.max(z))
cbar = fig.colorbar(cm.ScalarMappable(norm = norm), ax=ax)
cbar.ax.set_ylabel('Density')
return ax
if "__main__" == __name__ :
x = np.random.normal(size=100000)
y = x * 3 + np.random.normal(size=100000)
density_scatter( x, y, bins = [30,30] )
